A curated list of resources dedicated to evolutionary reinforcement learning.

Please feel free to pull requests

• Gangwani T, Peng J. Policy optimization by genetic distillation[J]. arXiv preprint arXiv:1711.01012, 2017.[Paper]

• Khadka S, Tumer K. Evolution-guided policy gradient in reinforcement learning[J]. Advances in Neural Information Processing Systems, 2018, 31.[Paper]
• Pourchot A, Sigaud O. CEM-RL: Combining evolutionary and gradient-based methods for policy search[J]. arXiv preprint arXiv:1810.01222, 2018.[Paper]
• Colas C, Sigaud O, Oudeyer P Y. Gep-pg: Decoupling exploration and exploitation in deep reinforcement learning algorithms[C]//International conference on machine learning. PMLR, 2018: 1039-1048.[Paper]
• Chang S, Yang J, Choi J, et al. Genetic-gated networks for deep reinforcement learning[J]. Advances in neural information processing systems, 2018, 31.[Paper]
• Kalashnikov D, Irpan A, Pastor P, et al. Scalable deep reinforcement learning for vision-based robotic manipulation[C]//Conference on Robot Learning. PMLR, 2018: 651-673.[Paper]

• Khadka S, Majumdar S, Nassar T, et al. Collaborative evolutionary reinforcement learning[C]//International conference on machine learning. PMLR, 2019: 3341-3350.[Paper]

• Bodnar C, Day B, Lió P. Proximal distilled evolutionary reinforcement learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(04): 3283-3290.[Paper]
• Franke J K H, Köhler G, Biedenkapp A, et al. Sample-efficient automated deep reinforcement learning[J]. arXiv preprint arXiv:2009.01555, 2020.[Paper]
• Marchesini E, Corsi D, Farinelli A. Genetic soft updates for policy evolution in deep reinforcement learning[C]//International Conference on Learning Representations. 2020.[Paper]
• Suri K, Shi X Q, Plataniotis K N, et al. Maximum mutation reinforcement learning for scalable control[J]. arXiv preprint arXiv:2007.13690, 2020.[Paper]
• Lee K, Lee B U, Shin U, et al. An efficient asynchronous method for integrating evolutionary and gradient-based policy search[J]. Advances in Neural Information Processing Systems, 2020, 33: 10124-10135.[Paper]
• Kim N, Baek H, Shin H. PGPS: Coupling Policy Gradient with Population-based Search[J]. 2020.[Paper]
• Jung W, Park G, Sung Y. Population-guided parallel policy search for reinforcement learning[J]. arXiv preprint arXiv:2001.02907, 2020.[Paper]

• Pretorius K W, Pillay N. Population based Reinforcement Learning[C]//2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2021: 1-8.[Paper]
• Liu J, Feng L. Diversity Evolutionary Policy Deep Reinforcement Learning[J]. Computational Intelligence and Neuroscience, 2021, 2021.[Paper]
• Lü S, Han S, Zhou W, et al. Recruitment-imitation mechanism for evolutionary reinforcement learning[J]. Information Sciences, 2021, 553: 172-188.[Paper]
• Majid A Y, Saaybi S, van Rietbergen T, et al. Deep reinforcement learning versus evolution strategies: A comparative survey[J]. arXiv preprint arXiv:2110.01411, 2021.[Paper]
• Nilsson O, Cully A. Policy gradient assisted map-elites[C]//Proceedings of the Genetic and Evolutionary Computation Conference. 2021: 866-875.[Paper]
• Wang Y, Xue K, Qian C. Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning[C]//International Conference on Learning Representations. 2021.[Paper]

• Ma Y, Liu T, Wei B, et al. Evolutionary Action Selection for Gradient-based Policy Learning[J]. arXiv preprint arXiv:2201.04286, 2022.[Paper]
• Wang Y, Zhang T, Chang Y, et al. A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning[J]. arXiv preprint arXiv:2201.00129, 2022.[Paper]
• Sigaud O. Combining Evolution and Deep Reinforcement Learning for Policy Search: a Survey[J]. arXiv preprint arXiv:2203.14009, 2022.[Paper]
• Shao L, You Y, Yan M, et al. Grac: Self-guided and self-regularized actor-critic[C]//Conference on Robot Learning. PMLR, 2022: 267-276.[Paper]
• Pierrot T, Macé V, Chalumeau F, et al. Diversity Policy Gradient for Sample Efficient Quality-Diversity Optimization[C]//ICLR Workshop on Agent Learning in Open-Endedness. 2022.[Paper]
• Li P, Tang H, Hao J, et al. ERL-Re $^ 2$: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation[J]. arXiv preprint arXiv:2210.17375, 2022.[Paper]